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Fall Detection Combining Android Accelerometer and Step Counting Virtual Sensors

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ICT for Health, Accessibility and Wellbeing (IHAW 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1799))

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Abstract

INTRODUCTION: Falls constitute a significant threat to older adults. Several approaches aimed at automatically detecting falls exist. Smartphones are widespread and can serve as a low-cost pervasive platform for automated fall detection. Existing fall detection apps are highly sensitive, but often suffers from sub-optimal specificity which can result in many false positives.

OBJECTIVES: The aim of this study was to investigate whether the built-in pedometer virtual sensor on the Android smartphone platform can be used to increase specificity and thereby achieve higher accuracy in an accelerometer-based Android fall detection application.

METHODS: An existing open threshold-based accelerometer algorithm was combined with the standard Android virtual sensor pedometer algorithm for detecting walking in the postfall phase. In a range of experiments, falls were simulated using a combination of a test mannequin and test participants, in order to determine the sensitivity and specificity of the solution.

RESULTS: All simulated falls were detected with 100% sensitivity. By counting postfall subsequent steps using the Android pedometer virtual sensor, the specificity of the application was increased to 100% in all scenarios.

CONCLUSION: The combination of accelerometer and pedometer sensors was found feasible to use for increasing the specificity of existing open fall detection algorithms.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Aarhus University, Finlandsgade 22, 8200, Aarhus, Denmark

    Jeppe Tinghøj Honoré, Rune Dalsenni Rask & Stefan Rahr Wagner

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  1. Jeppe Tinghøj Honoré
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Correspondence to Stefan Rahr Wagner .

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Editors and Affiliations

  1. University of Cyprus, Nicosia, Cyprus

    George Angelos Papadopoulos

  2. Frederick University, Nicosia, Cyprus

    Achilleas Achilleos

  3. Université de Rouen-Normandie, Mont Saint Aignan, France

    Edwige Pissaloux

  4. Universidad Panamericana, Aguascalientes, Mexico

    Ramiro Velázquez

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Honoré, J.T., Rask, R.D., Wagner, S.R. (2023). Fall Detection Combining Android Accelerometer and Step Counting Virtual Sensors. In: Papadopoulos, G.A., Achilleos, A., Pissaloux, E., Velázquez, R. (eds) ICT for Health, Accessibility and Wellbeing. IHAW 2022. Communications in Computer and Information Science, vol 1799. Springer, Cham. https://doi.org/10.1007/978-3-031-29548-5_1

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  • DOI: https://doi.org/10.1007/978-3-031-29548-5_1

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